Movie recommender class implemented

pom.xml

@@ -13,7 +13,6 @@
   <properties>
     <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
   </properties>
-
   <dependencies>
       <dependency>
           <groupId>org.apache.mahout</groupId>

src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java

@@ -0,0 +1,106 @@
+package nearsoft.academy.bigdata.recommendation;
+import org.apache.mahout.cf.taste.common.TasteException;
+import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
+import org.apache.mahout.cf.taste.impl.neighborhood.ThresholdUserNeighborhood;
+import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
+import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
+import org.apache.mahout.cf.taste.model.DataModel;
+import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
+import org.apache.mahout.cf.taste.recommender.UserBasedRecommender;
+import org.apache.mahout.cf.taste.recommender.RecommendedItem;
+import org.apache.mahout.cf.taste.similarity.UserSimilarity;
+
+import java.io.*;
+import java.util.*;
+import java.util.zip.GZIPInputStream;
+
+public class MovieRecommender {
+
+
+    private HashMap<String, Long> products = new HashMap();
+    private HashMap<Long, String> productsToString = new HashMap();
+    private HashMap<String, Long> users = new HashMap();
+    private int keyProducts = 0;
+    private int keyPUsers = 0;
+    int totalReviews = 0;
+    int totalProducts = 0;
+    int totalUsers = 0;
+
+
+    public MovieRecommender(String movies) throws IOException, TasteException {
+        createReccommendedFile(movies);
+
+    }
+
+    int getTotalReviews() throws IOException {
+        return totalReviews;
+    }
+
+    int getTotalProducts() throws FileNotFoundException, IOException {
+        return totalProducts;
+    }
+
+
+    int getTotalUsers() throws FileNotFoundException, IOException {
+        return totalUsers;
+    }
+
+    void createReccommendedFile(String pathToMovies) throws IOException, TasteException {
+        InputStream stream = new GZIPInputStream(new FileInputStream(pathToMovies));
+        BufferedReader b = new BufferedReader(new InputStreamReader(stream, "US-ASCII"));
+        String line;
+        File csv = new File("recommenderFile.csv");
+        if (!csv.exists()) {
+            csv.createNewFile();
+        }
+        FileWriter fw = new FileWriter(csv);
+        BufferedWriter bw = new BufferedWriter(fw);
+        long productId = 0, userId = 0;
+        while((line = b.readLine())!=null) {
+            String prefix = line.split(" ")[0];
+            if(prefix.equals("product/productId:")) {
+                String pdID = line.split(" ")[1];
+                if (products.containsKey(pdID)) {
+                    productId = products.get(pdID);
+                }else {
+                    products.put(pdID, (long) keyProducts);
+                    productsToString.put((long) keyProducts, pdID);
+                    productId = keyProducts;
+                    keyProducts++;
+                    totalProducts++;
+                }
+            }
+            if(prefix.equals("review/userId:")){
+                String usID = line.split(" ")[1];
+                if (users.containsKey(usID)) {
+                    userId = users.get(usID);
+                }else {
+                    users.put(usID, (long) keyPUsers);
+                    userId = keyPUsers;
+                    keyPUsers++;
+                    totalUsers++;
+                }
+            }
+            if(prefix.equals("review/score:")){
+                totalReviews++;
+                String rvSC = line.split(" ")[1];
+                bw.write(userId + "," + productId + "," + rvSC+"\n");
+            }
+        }
+        bw.close();
+    }
+
+    public List<String> getRecommendationsForUser(String user) throws IOException, TasteException {
+        List<String> stringList = new ArrayList<String>();
+        DataModel model = new FileDataModel(new File("recommenderFile.csv"));
+        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+        UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+        UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+        for (RecommendedItem recommendation : recommender.recommend(users.get(user), 3)) {
+            stringList.add(productsToString.get(recommendation.getItemID()));
+        }
+        return stringList;
+    }
+
+
+}

src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommenderTest.java

@@ -15,7 +15,7 @@ public class MovieRecommenderTest {
     public void testDataInfo() throws IOException, TasteException {
         //download movies.txt.gz from 
         //    http://snap.stanford.edu/data/web-Movies.html
-        MovieRecommender recommender = new MovieRecommender("/path/to/movies.txt.gz");
+        MovieRecommender recommender = new MovieRecommender("/Users/lromero/Desktop/LuisMario/AmazonProblem/big-data-exercises/src/test/java/nearsoft/academy/bigdata/recommendation/movies.txt.gz");
         assertEquals(7911684, recommender.getTotalReviews());
         assertEquals(253059, recommender.getTotalProducts());
         assertEquals(889176, recommender.getTotalUsers());
@@ -24,7 +24,5 @@ public void testDataInfo() throws IOException, TasteException {
         assertThat(recommendations, hasItem("B0002O7Y8U"));
         assertThat(recommendations, hasItem("B00004CQTF"));
         assertThat(recommendations, hasItem("B000063W82"));
-
     }
-
 }